Lifting Health Professionals’ Morale During the COVID-19 Pandemic: Moderating Emotions to Support Ethical Decisions
Bibliographic record
Abstract
The current COVID-19 pandemic creates a difficult and unprecedented time. With each passing day, the care of the health team itself is essential; and not only physical care, but also for mental health. The authors describe their experience in disseminating recommendations through short videos to help professionals maintain an objective view of the reality they are experiencing. Thus, knowing how to tabulate daily the evolution of the patients that each professional has been entrusted to care for – the hospitalized, the deaths and, very importantly, the discharge of the recovered – provides a sense of reality. Cinema, an educational resource used in medical education, which is also included in these videos, helps to clarify the recommendations made above and to maintain emotional balance. The authors conclude that providing a realistic view of the situation that the team is experiencing in this crisis and highlighting the positive facts and achievements could be a valuable means of help from medical educators behind the scenes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".